Overview

Dataset statistics

Number of variables15
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.3 KiB
Average record size in memory120.1 B

Variable types

Numeric6
Categorical9

Alerts

race is highly imbalanced (65.3%)Imbalance
native-country is highly imbalanced (82.1%)Imbalance
capital-gain has 919 (91.9%) zerosZeros
capital-loss has 950 (95.0%) zerosZeros

Reproduction

Analysis started2024-03-03 09:42:44.214478
Analysis finished2024-03-03 09:42:58.023847
Duration13.81 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct66
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.051
Minimum17
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:42:58.200604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile19
Q128
median36
Q346
95-th percentile63
Maximum90
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.34948
Coefficient of variation (CV)0.35083124
Kurtosis-0.042832687
Mean38.051
Median Absolute Deviation (MAD)9
Skewness0.58910654
Sum38051
Variance178.20861
MonotonicityNot monotonic
2024-03-03T09:42:58.599461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 33
 
3.3%
43 33
 
3.3%
34 32
 
3.2%
23 30
 
3.0%
33 30
 
3.0%
44 28
 
2.8%
35 28
 
2.8%
24 28
 
2.8%
36 28
 
2.8%
42 28
 
2.8%
Other values (56) 702
70.2%
ValueCountFrequency (%)
17 20
2.0%
18 16
1.6%
19 21
2.1%
20 23
2.3%
21 17
1.7%
22 22
2.2%
23 30
3.0%
24 28
2.8%
25 23
2.3%
26 21
2.1%
ValueCountFrequency (%)
90 1
0.1%
81 1
0.1%
80 1
0.1%
79 1
0.1%
78 1
0.1%
77 1
0.1%
76 2
0.2%
75 1
0.1%
74 1
0.1%
73 1
0.1%

workclass
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Private
698 
Self-emp-not-inc
81 
Local-gov
 
68
?
 
62
State-gov
 
37
Other values (2)
 
54

Length

Max length17
Median length8
Mean length8.816
Min length2

Characters and Unicode

Total characters8816
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row State-gov
2nd row Self-emp-not-inc
3rd row Private
4th row Private
5th row Private

Common Values

ValueCountFrequency (%)
Private 698
69.8%
Self-emp-not-inc 81
 
8.1%
Local-gov 68
 
6.8%
? 62
 
6.2%
State-gov 37
 
3.7%
Self-emp-inc 33
 
3.3%
Federal-gov 21
 
2.1%

Length

2024-03-03T09:42:59.092307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:42:59.415883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
private 698
69.8%
self-emp-not-inc 81
 
8.1%
local-gov 68
 
6.8%
62
 
6.2%
state-gov 37
 
3.7%
self-emp-inc 33
 
3.3%
federal-gov 21
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 1005
11.4%
1000
11.3%
t 853
9.7%
a 824
9.3%
v 824
9.3%
i 812
9.2%
r 719
8.2%
P 698
7.9%
- 435
 
4.9%
o 275
 
3.1%
Other values (12) 1371
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6381
72.4%
Space Separator 1000
 
11.3%
Uppercase Letter 938
 
10.6%
Dash Punctuation 435
 
4.9%
Other Punctuation 62
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1005
15.7%
t 853
13.4%
a 824
12.9%
v 824
12.9%
i 812
12.7%
r 719
11.3%
o 275
 
4.3%
l 203
 
3.2%
n 195
 
3.1%
c 182
 
2.9%
Other values (5) 489
7.7%
Uppercase Letter
ValueCountFrequency (%)
P 698
74.4%
S 151
 
16.1%
L 68
 
7.2%
F 21
 
2.2%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 435
100.0%
Other Punctuation
ValueCountFrequency (%)
? 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7319
83.0%
Common 1497
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1005
13.7%
t 853
11.7%
a 824
11.3%
v 824
11.3%
i 812
11.1%
r 719
9.8%
P 698
9.5%
o 275
 
3.8%
l 203
 
2.8%
n 195
 
2.7%
Other values (9) 911
12.4%
Common
ValueCountFrequency (%)
1000
66.8%
- 435
29.1%
? 62
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1005
11.4%
1000
11.3%
t 853
9.7%
a 824
9.3%
v 824
9.3%
i 812
9.2%
r 719
8.2%
P 698
7.9%
- 435
 
4.9%
o 275
 
3.1%
Other values (12) 1371
15.6%

fnlwgt
Real number (ℝ)

Distinct987
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191904.98
Minimum21174
Maximum1033222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:42:59.756657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21174
5-th percentile43274.15
Q1115041.25
median180590.5
Q3247152.25
95-th percentile382841.9
Maximum1033222
Range1012048
Interquartile range (IQR)132111

Descriptive statistics

Standard deviation108125.54
Coefficient of variation (CV)0.56343272
Kurtosis5.7469451
Mean191904.98
Median Absolute Deviation (MAD)65749
Skewness1.483933
Sum1.9190498 × 108
Variance1.1691133 × 1010
MonotonicityNot monotonic
2024-03-03T09:43:00.242378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116632 3
 
0.3%
108293 2
 
0.2%
32185 2
 
0.2%
217460 2
 
0.2%
111567 2
 
0.2%
368700 2
 
0.2%
182556 2
 
0.2%
191277 2
 
0.2%
92262 2
 
0.2%
194636 2
 
0.2%
Other values (977) 979
97.9%
ValueCountFrequency (%)
21174 1
0.1%
21906 1
0.1%
22463 1
0.1%
23780 1
0.1%
24215 1
0.1%
25429 1
0.1%
25826 1
0.1%
25828 1
0.1%
27053 1
0.1%
27337 1
0.1%
ValueCountFrequency (%)
1033222 1
0.1%
860348 1
0.1%
680390 1
0.1%
635913 1
0.1%
633742 1
0.1%
556660 1
0.1%
544091 1
0.1%
543162 1
0.1%
543028 1
0.1%
538583 1
0.1%

education
Categorical

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
HS-grad
321 
Some-college
225 
Bachelors
166 
Masters
54 
Assoc-voc
48 
Other values (11)
186 

Length

Max length13
Median length12
Mean length9.438
Min length4

Characters and Unicode

Total characters9438
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Bachelors
2nd row Bachelors
3rd row HS-grad
4th row 11th
5th row Bachelors

Common Values

ValueCountFrequency (%)
HS-grad 321
32.1%
Some-college 225
22.5%
Bachelors 166
16.6%
Masters 54
 
5.4%
Assoc-voc 48
 
4.8%
11th 46
 
4.6%
Assoc-acdm 35
 
3.5%
10th 21
 
2.1%
9th 16
 
1.6%
7th-8th 15
 
1.5%
Other values (6) 53
 
5.3%

Length

2024-03-03T09:43:00.644656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hs-grad 321
32.1%
some-college 225
22.5%
bachelors 166
16.6%
masters 54
 
5.4%
assoc-voc 48
 
4.8%
11th 46
 
4.6%
assoc-acdm 35
 
3.5%
10th 21
 
2.1%
9th 16
 
1.6%
7th-8th 15
 
1.5%
Other values (6) 53
 
5.3%

Most occurring characters

ValueCountFrequency (%)
1000
 
10.6%
e 911
 
9.7%
o 809
 
8.6%
- 672
 
7.1%
l 628
 
6.7%
a 590
 
6.3%
c 583
 
6.2%
r 567
 
6.0%
S 546
 
5.8%
g 546
 
5.8%
Other values (22) 2586
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6336
67.1%
Uppercase Letter 1196
 
12.7%
Space Separator 1000
 
10.6%
Dash Punctuation 672
 
7.1%
Decimal Number 234
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 911
14.4%
o 809
12.8%
l 628
9.9%
a 590
9.3%
c 583
9.2%
r 567
8.9%
g 546
8.6%
s 459
7.2%
d 356
 
5.6%
h 329
 
5.2%
Other values (4) 558
8.8%
Decimal Number
ValueCountFrequency (%)
1 129
55.1%
0 21
 
9.0%
9 16
 
6.8%
7 15
 
6.4%
8 15
 
6.4%
5 11
 
4.7%
6 11
 
4.7%
2 9
 
3.8%
4 7
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
S 546
45.7%
H 321
26.8%
B 166
 
13.9%
A 83
 
6.9%
M 54
 
4.5%
D 14
 
1.2%
P 12
 
1.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7532
79.8%
Common 1906
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 911
12.1%
o 809
10.7%
l 628
8.3%
a 590
 
7.8%
c 583
 
7.7%
r 567
 
7.5%
S 546
 
7.2%
g 546
 
7.2%
s 459
 
6.1%
d 356
 
4.7%
Other values (11) 1537
20.4%
Common
ValueCountFrequency (%)
1000
52.5%
- 672
35.3%
1 129
 
6.8%
0 21
 
1.1%
9 16
 
0.8%
7 15
 
0.8%
8 15
 
0.8%
5 11
 
0.6%
6 11
 
0.6%
2 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1000
 
10.6%
e 911
 
9.7%
o 809
 
8.6%
- 672
 
7.1%
l 628
 
6.7%
a 590
 
6.3%
c 583
 
6.2%
r 567
 
6.0%
S 546
 
5.8%
g 546
 
5.8%
Other values (22) 2586
27.4%

education-num
Real number (ℝ)

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.084
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:43:01.345087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q312
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5486152
Coefficient of variation (CV)0.25273852
Kurtosis0.84022152
Mean10.084
Median Absolute Deviation (MAD)1
Skewness-0.37139661
Sum10084
Variance6.4954394
MonotonicityNot monotonic
2024-03-03T09:43:01.682385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 321
32.1%
10 225
22.5%
13 166
16.6%
14 54
 
5.4%
11 48
 
4.8%
7 46
 
4.6%
12 35
 
3.5%
6 21
 
2.1%
5 16
 
1.6%
4 15
 
1.5%
Other values (6) 53
 
5.3%
ValueCountFrequency (%)
1 2
 
0.2%
2 7
 
0.7%
3 11
 
1.1%
4 15
 
1.5%
5 16
 
1.6%
6 21
 
2.1%
7 46
 
4.6%
8 9
 
0.9%
9 321
32.1%
10 225
22.5%
ValueCountFrequency (%)
16 14
 
1.4%
15 10
 
1.0%
14 54
 
5.4%
13 166
16.6%
12 35
 
3.5%
11 48
 
4.8%
10 225
22.5%
9 321
32.1%
8 9
 
0.9%
7 46
 
4.6%

marital-status
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Married-civ-spouse
443 
Never-married
344 
Divorced
136 
Widowed
 
33
Separated
 
28
Other values (2)
 
16

Length

Max length22
Median length19
Mean length15.349
Min length8

Characters and Unicode

Total characters15349
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row Never-married
2nd row Married-civ-spouse
3rd row Divorced
4th row Married-civ-spouse
5th row Married-civ-spouse

Common Values

ValueCountFrequency (%)
Married-civ-spouse 443
44.3%
Never-married 344
34.4%
Divorced 136
 
13.6%
Widowed 33
 
3.3%
Separated 28
 
2.8%
Married-spouse-absent 15
 
1.5%
Married-AF-spouse 1
 
0.1%

Length

2024-03-03T09:43:02.124086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:43:02.502804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
married-civ-spouse 443
44.3%
never-married 344
34.4%
divorced 136
 
13.6%
widowed 33
 
3.3%
separated 28
 
2.8%
married-spouse-absent 15
 
1.5%
married-af-spouse 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2190
14.3%
r 2114
13.8%
i 1415
9.2%
- 1262
8.2%
d 1033
 
6.7%
1000
 
6.5%
s 933
 
6.1%
v 923
 
6.0%
a 874
 
5.7%
o 628
 
4.1%
Other values (15) 2977
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12085
78.7%
Dash Punctuation 1262
 
8.2%
Uppercase Letter 1002
 
6.5%
Space Separator 1000
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2190
18.1%
r 2114
17.5%
i 1415
11.7%
d 1033
8.5%
s 933
7.7%
v 923
7.6%
a 874
 
7.2%
o 628
 
5.2%
c 579
 
4.8%
p 487
 
4.0%
Other values (6) 909
7.5%
Uppercase Letter
ValueCountFrequency (%)
M 459
45.8%
N 344
34.3%
D 136
 
13.6%
W 33
 
3.3%
S 28
 
2.8%
A 1
 
0.1%
F 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1262
100.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13087
85.3%
Common 2262
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2190
16.7%
r 2114
16.2%
i 1415
10.8%
d 1033
7.9%
s 933
7.1%
v 923
7.1%
a 874
 
6.7%
o 628
 
4.8%
c 579
 
4.4%
p 487
 
3.7%
Other values (13) 1911
14.6%
Common
ValueCountFrequency (%)
- 1262
55.8%
1000
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2190
14.3%
r 2114
13.8%
i 1415
9.2%
- 1262
8.2%
d 1033
 
6.7%
1000
 
6.5%
s 933
 
6.1%
v 923
 
6.0%
a 874
 
5.7%
o 628
 
4.1%
Other values (15) 2977
19.4%

occupation
Categorical

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Craft-repair
126 
Prof-specialty
124 
Exec-managerial
124 
Sales
112 
Other-service
107 
Other values (10)
407 

Length

Max length18
Median length16
Mean length13.139
Min length2

Characters and Unicode

Total characters13139
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row Adm-clerical
2nd row Exec-managerial
3rd row Handlers-cleaners
4th row Handlers-cleaners
5th row Prof-specialty

Common Values

ValueCountFrequency (%)
Craft-repair 126
12.6%
Prof-specialty 124
12.4%
Exec-managerial 124
12.4%
Sales 112
11.2%
Other-service 107
10.7%
Adm-clerical 94
9.4%
? 62
6.2%
Machine-op-inspct 61
6.1%
Transport-moving 52
5.2%
Tech-support 44
 
4.4%
Other values (5) 94
9.4%

Length

2024-03-03T09:43:03.047771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
craft-repair 126
12.6%
prof-specialty 124
12.4%
exec-managerial 124
12.4%
sales 112
11.2%
other-service 107
10.7%
adm-clerical 94
9.4%
62
6.2%
machine-op-inspct 61
6.1%
transport-moving 52
5.2%
tech-support 44
 
4.4%
Other values (5) 94
9.4%

Most occurring characters

ValueCountFrequency (%)
e 1315
 
10.0%
r 1239
 
9.4%
a 1184
 
9.0%
1000
 
7.6%
- 890
 
6.8%
i 861
 
6.6%
c 769
 
5.9%
s 640
 
4.9%
l 634
 
4.8%
t 546
 
4.2%
Other values (23) 4061
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10248
78.0%
Space Separator 1000
 
7.6%
Uppercase Letter 939
 
7.1%
Dash Punctuation 890
 
6.8%
Other Punctuation 62
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1315
12.8%
r 1239
12.1%
a 1184
11.6%
i 861
8.4%
c 769
 
7.5%
s 640
 
6.2%
l 634
 
6.2%
t 546
 
5.3%
p 512
 
5.0%
n 498
 
4.9%
Other values (10) 2050
20.0%
Uppercase Letter
ValueCountFrequency (%)
P 143
15.2%
C 126
13.4%
E 124
13.2%
S 112
11.9%
O 107
11.4%
T 96
10.2%
A 95
10.1%
M 61
6.5%
H 43
 
4.6%
F 32
 
3.4%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 890
100.0%
Other Punctuation
ValueCountFrequency (%)
? 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11187
85.1%
Common 1952
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1315
11.8%
r 1239
11.1%
a 1184
 
10.6%
i 861
 
7.7%
c 769
 
6.9%
s 640
 
5.7%
l 634
 
5.7%
t 546
 
4.9%
p 512
 
4.6%
n 498
 
4.5%
Other values (20) 2989
26.7%
Common
ValueCountFrequency (%)
1000
51.2%
- 890
45.6%
? 62
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1315
 
10.0%
r 1239
 
9.4%
a 1184
 
9.0%
1000
 
7.6%
- 890
 
6.8%
i 861
 
6.6%
c 769
 
5.9%
s 640
 
4.9%
l 634
 
4.8%
t 546
 
4.2%
Other values (23) 4061
30.9%

relationship
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Husband
376 
Not-in-family
279 
Own-child
151 
Unmarried
109 
Wife
61 

Length

Max length15
Median length14
Mean length10.179
Min length5

Characters and Unicode

Total characters10179
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Not-in-family
2nd row Husband
3rd row Not-in-family
4th row Husband
5th row Wife

Common Values

ValueCountFrequency (%)
Husband 376
37.6%
Not-in-family 279
27.9%
Own-child 151
15.1%
Unmarried 109
 
10.9%
Wife 61
 
6.1%
Other-relative 24
 
2.4%

Length

2024-03-03T09:43:03.478525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:43:03.803609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
husband 376
37.6%
not-in-family 279
27.9%
own-child 151
15.1%
unmarried 109
 
10.9%
wife 61
 
6.1%
other-relative 24
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1000
 
9.8%
n 915
 
9.0%
i 903
 
8.9%
a 788
 
7.7%
- 733
 
7.2%
d 636
 
6.2%
l 454
 
4.5%
m 388
 
3.8%
H 376
 
3.7%
u 376
 
3.7%
Other values (16) 3610
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7446
73.2%
Space Separator 1000
 
9.8%
Uppercase Letter 1000
 
9.8%
Dash Punctuation 733
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 915
12.3%
i 903
12.1%
a 788
10.6%
d 636
 
8.5%
l 454
 
6.1%
m 388
 
5.2%
u 376
 
5.0%
s 376
 
5.0%
b 376
 
5.0%
f 340
 
4.6%
Other values (9) 1894
25.4%
Uppercase Letter
ValueCountFrequency (%)
H 376
37.6%
N 279
27.9%
O 175
17.5%
U 109
 
10.9%
W 61
 
6.1%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8446
83.0%
Common 1733
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 915
 
10.8%
i 903
 
10.7%
a 788
 
9.3%
d 636
 
7.5%
l 454
 
5.4%
m 388
 
4.6%
H 376
 
4.5%
u 376
 
4.5%
s 376
 
4.5%
b 376
 
4.5%
Other values (14) 2858
33.8%
Common
ValueCountFrequency (%)
1000
57.7%
- 733
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1000
 
9.8%
n 915
 
9.0%
i 903
 
8.9%
a 788
 
7.7%
- 733
 
7.2%
d 636
 
6.2%
l 454
 
4.5%
m 388
 
3.8%
H 376
 
3.7%
u 376
 
3.7%
Other values (16) 3610
35.5%

race
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
White
847 
Black
110 
Asian-Pac-Islander
 
27
Amer-Indian-Eskimo
 
10
Other
 
6

Length

Max length19
Median length6
Mean length6.481
Min length6

Characters and Unicode

Total characters6481
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row White
2nd row White
3rd row White
4th row Black
5th row Black

Common Values

ValueCountFrequency (%)
White 847
84.7%
Black 110
 
11.0%
Asian-Pac-Islander 27
 
2.7%
Amer-Indian-Eskimo 10
 
1.0%
Other 6
 
0.6%

Length

2024-03-03T09:43:04.195641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:43:04.486611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 847
84.7%
black 110
 
11.0%
asian-pac-islander 27
 
2.7%
amer-indian-eskimo 10
 
1.0%
other 6
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1000
15.4%
i 894
13.8%
e 890
13.7%
t 853
13.2%
h 853
13.2%
W 847
13.1%
a 201
 
3.1%
c 137
 
2.1%
l 137
 
2.1%
k 120
 
1.9%
Other values (13) 549
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4333
66.9%
Uppercase Letter 1074
 
16.6%
Space Separator 1000
 
15.4%
Dash Punctuation 74
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 894
20.6%
e 890
20.5%
t 853
19.7%
h 853
19.7%
a 201
 
4.6%
c 137
 
3.2%
l 137
 
3.2%
k 120
 
2.8%
n 74
 
1.7%
s 64
 
1.5%
Other values (4) 110
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
W 847
78.9%
B 110
 
10.2%
A 37
 
3.4%
I 37
 
3.4%
P 27
 
2.5%
E 10
 
0.9%
O 6
 
0.6%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5407
83.4%
Common 1074
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 894
16.5%
e 890
16.5%
t 853
15.8%
h 853
15.8%
W 847
15.7%
a 201
 
3.7%
c 137
 
2.5%
l 137
 
2.5%
k 120
 
2.2%
B 110
 
2.0%
Other values (11) 365
6.8%
Common
ValueCountFrequency (%)
1000
93.1%
- 74
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1000
15.4%
i 894
13.8%
e 890
13.7%
t 853
13.2%
h 853
13.2%
W 847
13.1%
a 201
 
3.1%
c 137
 
2.1%
l 137
 
2.1%
k 120
 
1.9%
Other values (13) 549
8.5%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Male
671 
Female
329 

Length

Max length7
Median length5
Mean length5.658
Min length5

Characters and Unicode

Total characters5658
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row Male
2nd row Male
3rd row Male
4th row Male
5th row Female

Common Values

ValueCountFrequency (%)
Male 671
67.1%
Female 329
32.9%

Length

2024-03-03T09:43:04.849260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:43:05.140904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
male 671
67.1%
female 329
32.9%

Most occurring characters

ValueCountFrequency (%)
e 1329
23.5%
a 1000
17.7%
1000
17.7%
l 1000
17.7%
M 671
11.9%
F 329
 
5.8%
m 329
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3658
64.7%
Space Separator 1000
 
17.7%
Uppercase Letter 1000
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1329
36.3%
a 1000
27.3%
l 1000
27.3%
m 329
 
9.0%
Uppercase Letter
ValueCountFrequency (%)
M 671
67.1%
F 329
32.9%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4658
82.3%
Common 1000
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1329
28.5%
a 1000
21.5%
l 1000
21.5%
M 671
14.4%
F 329
 
7.1%
m 329
 
7.1%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1329
23.5%
a 1000
17.7%
1000
17.7%
l 1000
17.7%
M 671
11.9%
F 329
 
5.8%
m 329
 
5.8%

capital-gain
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.526
Minimum0
Maximum34095
Zeros919
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:43:05.426343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4115.25
Maximum34095
Range34095
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2618.5375
Coefficient of variation (CV)4.4493149
Kurtosis49.452783
Mean588.526
Median Absolute Deviation (MAD)0
Skewness6.2480627
Sum588526
Variance6856738.7
MonotonicityNot monotonic
2024-03-03T09:43:05.803878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 919
91.9%
15024 11
 
1.1%
7688 8
 
0.8%
7298 8
 
0.8%
4386 5
 
0.5%
2174 4
 
0.4%
5178 4
 
0.4%
14084 3
 
0.3%
1055 3
 
0.3%
594 3
 
0.3%
Other values (26) 32
 
3.2%
ValueCountFrequency (%)
0 919
91.9%
594 3
 
0.3%
1055 3
 
0.3%
1111 1
 
0.1%
1409 1
 
0.1%
2050 1
 
0.1%
2174 4
 
0.4%
2176 1
 
0.1%
2407 3
 
0.3%
2463 1
 
0.1%
ValueCountFrequency (%)
34095 1
 
0.1%
25236 1
 
0.1%
20051 1
 
0.1%
15024 11
1.1%
14344 1
 
0.1%
14084 3
 
0.3%
10605 1
 
0.1%
9386 1
 
0.1%
8614 1
 
0.1%
7688 8
0.8%

capital-loss
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.96
Minimum0
Maximum2415
Zeros950
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:43:06.149831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32.65
Maximum2415
Range2415
Interquartile range (IQR)0

Descriptive statistics

Standard deviation412.44234
Coefficient of variation (CV)4.4367721
Kurtosis17.451486
Mean92.96
Median Absolute Deviation (MAD)0
Skewness4.3441325
Sum92960
Variance170108.68
MonotonicityNot monotonic
2024-03-03T09:43:06.499344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 950
95.0%
1977 6
 
0.6%
1902 5
 
0.5%
1887 4
 
0.4%
2415 3
 
0.3%
1762 2
 
0.2%
1980 2
 
0.2%
1564 2
 
0.2%
2179 2
 
0.2%
1408 2
 
0.2%
Other values (20) 22
 
2.2%
ValueCountFrequency (%)
0 950
95.0%
653 1
 
0.1%
1340 1
 
0.1%
1380 2
 
0.2%
1408 2
 
0.2%
1485 1
 
0.1%
1504 1
 
0.1%
1564 2
 
0.2%
1573 1
 
0.1%
1669 1
 
0.1%
ValueCountFrequency (%)
2415 3
0.3%
2392 1
 
0.1%
2377 1
 
0.1%
2352 1
 
0.1%
2339 1
 
0.1%
2206 1
 
0.1%
2179 2
0.2%
2051 1
 
0.1%
2042 1
 
0.1%
1980 2
0.2%

hours-per-week
Real number (ℝ)

Distinct56
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.876
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-03T09:43:06.934909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q140
median40
Q345
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.018114
Coefficient of variation (CV)0.30138714
Kurtosis2.3381995
Mean39.876
Median Absolute Deviation (MAD)2
Skewness-0.0036032951
Sum39876
Variance144.43506
MonotonicityNot monotonic
2024-03-03T09:43:07.421253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 472
47.2%
50 79
 
7.9%
45 61
 
6.1%
60 47
 
4.7%
20 44
 
4.4%
35 38
 
3.8%
30 28
 
2.8%
25 21
 
2.1%
55 21
 
2.1%
38 18
 
1.8%
Other values (46) 171
 
17.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 2
 
0.2%
4 1
 
0.1%
5 3
0.3%
6 3
0.3%
7 1
 
0.1%
8 3
0.3%
9 1
 
0.1%
10 7
0.7%
12 4
0.4%
ValueCountFrequency (%)
99 1
 
0.1%
98 1
 
0.1%
80 5
 
0.5%
75 2
 
0.2%
72 2
 
0.2%
70 9
 
0.9%
65 4
 
0.4%
64 2
 
0.2%
60 47
4.7%
59 1
 
0.1%

native-country
Categorical

IMBALANCE 

Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
United-States
902 
Mexico
 
20
?
 
18
Puerto-Rico
 
4
Cuba
 
4
Other values (24)
 
52

Length

Max length19
Median length14
Mean length13.304
Min length2

Characters and Unicode

Total characters13304
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.7%

Sample

1st row United-States
2nd row United-States
3rd row United-States
4th row United-States
5th row Cuba

Common Values

ValueCountFrequency (%)
United-States 902
90.2%
Mexico 20
 
2.0%
? 18
 
1.8%
Puerto-Rico 4
 
0.4%
Cuba 4
 
0.4%
Portugal 4
 
0.4%
Philippines 4
 
0.4%
Germany 3
 
0.3%
India 3
 
0.3%
Iran 3
 
0.3%
Other values (19) 35
 
3.5%

Length

2024-03-03T09:43:07.844045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united-states 902
90.2%
mexico 20
 
2.0%
18
 
1.8%
puerto-rico 4
 
0.4%
cuba 4
 
0.4%
portugal 4
 
0.4%
philippines 4
 
0.4%
germany 3
 
0.3%
india 3
 
0.3%
iran 3
 
0.3%
Other values (19) 35
 
3.5%

Most occurring characters

ValueCountFrequency (%)
t 2723
20.5%
e 1840
13.8%
1000
 
7.5%
a 971
 
7.3%
i 959
 
7.2%
n 938
 
7.1%
d 921
 
6.9%
- 910
 
6.8%
s 909
 
6.8%
S 907
 
6.8%
Other values (29) 1226
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9484
71.3%
Uppercase Letter 1892
 
14.2%
Space Separator 1000
 
7.5%
Dash Punctuation 910
 
6.8%
Other Punctuation 18
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2723
28.7%
e 1840
19.4%
a 971
 
10.2%
i 959
 
10.1%
n 938
 
9.9%
d 921
 
9.7%
s 909
 
9.6%
o 48
 
0.5%
c 32
 
0.3%
l 26
 
0.3%
Other values (11) 117
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
S 907
47.9%
U 902
47.7%
M 20
 
1.1%
P 15
 
0.8%
C 10
 
0.5%
I 8
 
0.4%
R 6
 
0.3%
E 6
 
0.3%
G 5
 
0.3%
H 4
 
0.2%
Other values (5) 9
 
0.5%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 910
100.0%
Other Punctuation
ValueCountFrequency (%)
? 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11376
85.5%
Common 1928
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2723
23.9%
e 1840
16.2%
a 971
 
8.5%
i 959
 
8.4%
n 938
 
8.2%
d 921
 
8.1%
s 909
 
8.0%
S 907
 
8.0%
U 902
 
7.9%
o 48
 
0.4%
Other values (26) 258
 
2.3%
Common
ValueCountFrequency (%)
1000
51.9%
- 910
47.2%
? 18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2723
20.5%
e 1840
13.8%
1000
 
7.5%
a 971
 
7.3%
i 959
 
7.2%
n 938
 
7.1%
d 921
 
6.9%
- 910
 
6.8%
s 909
 
6.8%
S 907
 
6.8%
Other values (29) 1226
9.2%

salary
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
768 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Length

2024-03-03T09:43:08.283765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T09:43:08.543894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Most occurring characters

ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 768
76.8%
1 232
 
23.2%

Interactions

2024-03-03T09:42:55.330714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:45.984593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:47.967008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:49.994066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:51.794453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:53.575177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:55.657213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:46.281213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:48.218322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:50.309069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:52.097485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:53.862973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:55.956750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:46.609539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:48.502711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:50.559094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:52.375978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:54.122142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:56.221272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:46.877917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:49.083585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:50.867686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:52.664888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:54.390264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:56.472173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:47.225828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:49.428082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:51.197490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:52.980442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:54.703337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:56.721512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:47.601642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:49.703449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:51.511487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:53.259181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T09:42:55.048013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-03T09:42:57.154977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T09:42:57.758496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrysalary
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States0
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States0
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States0
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States0
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba0
537Private284582Masters14Married-civ-spouseExec-managerialWifeWhiteFemale0040United-States0
649Private1601879th5Married-spouse-absentOther-serviceNot-in-familyBlackFemale0016Jamaica0
752Self-emp-not-inc209642HS-grad9Married-civ-spouseExec-managerialHusbandWhiteMale0045United-States1
831Private45781Masters14Never-marriedProf-specialtyNot-in-familyWhiteFemale14084050United-States1
942Private159449Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale5178040United-States1
ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrysalary
99046Private187370Bachelors13Never-marriedSalesNot-in-familyWhiteMale0150440United-States0
99141Private194636Assoc-voc11Married-civ-spouseMachine-op-inspctHusbandWhiteMale0040United-States0
99250Self-emp-not-inc124793HS-grad9Married-civ-spouseCraft-repairHusbandWhiteMale0030United-States0
99347Private192835HS-grad9Married-civ-spouseAdm-clericalHusbandWhiteMale0050United-States1
99435Private290226HS-grad9Never-marriedExec-managerialNot-in-familyWhiteMale0045United-States0
99556Private112840HS-grad9Married-civ-spouseExec-managerialHusbandWhiteMale0055United-States1
99645Private89325Masters14DivorcedProf-specialtyNot-in-familyWhiteMale0045United-States0
99748Federal-gov33109Bachelors13DivorcedExec-managerialUnmarriedWhiteMale0058United-States1
99840Private82465Some-college10Married-civ-spouseMachine-op-inspctHusbandWhiteMale2580040United-States0
99939Self-emp-inc329980Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale15024050United-States1